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5 Reasons Analytics Projects Can Fail

Five common technical, behavioral, and organizational barriers prevent some business analytics projects from fulfilling their goals of revealing actionable insights and facilitating more predictive decision-making.

Business analytics projects tend to make big promises. Among them, the projects propose to give executives a better understanding of their current business environment and to help them anticipate future business conditions; to facilitate more predictive and prescriptive decision-making; and to uncover “actionable insights” latent in databases ranging from traditional legacy systems to diverse external data sources (including sensors, “smart” technologies, and external vendor-provided data) to cutting-edge social media data repositories.

Too often, business analytics efforts fail to fulfill these lofty promises. Some perish before they get started. Others reach completion but fail to realize their full potential due to limited implementations, faulty technology integration, and challenged change management efforts. They get hung up on several common technical, behavioral, and organizational barriers that can either prevent these projects from gaining momentum or preclude them from delivering the desired results.

“Many organizations spend years deliberating—often out of a combination of organizational inertia, competing priorities, and skepticism of business analytics—before taking the first step toward embracing these methods,” says John Lucker, principal and Global Advanced Analytics & Modeling market leader for Deloitte Consulting LLP. “Challenges often stem from office politics, misconceptions about analytics, and fruitless quests for data and statistical perfection.”

Here, Lucker and colleague James Guszcza outline five common hurdles organizations face when pursuing business analytics initiatives. (In a related article, Predictive Analytics: Why Your CEO Insists on Using His Gut, Lucker and Guszcza explain why top executives are often the most skeptical of analytics projects.)

1. Misconceptions about analytics. It’s not uncommon for some stakeholders to be skeptical of the value of analytics. Their skepticism frequently stems from misconceptions about analytics and what it can and can’t do, says Lucker. Because business analytics is sometimes mistaken for off-the-shelf software somehow purporting to “predict the future,” some dismiss it as a smoke-and-mirrors business fad. Others erroneously believe that analytics solutions will provide them with a kind of absolute truth.

Both notions about analytics software are detrimental to analytics projects, says Guszcza, national predictive analytics lead for Deloitte Consulting LLP’s Advanced Analytics & Modeling practice. The former notion makes business analytics seem too good to be true, which makes it difficult to sell the concept within the organization, he says. The latter can lead to unrealistic expectations—and disappointment—when predictive analytical models fail to provide absolute truth. It can also lead companies to rely on analytics alone to make complicated decisions when, in fact, they should bring in the professional judgment and domain knowledge of skilled employees who can provide proper checks and balances, he adds.

Lucker warns organizations against the pursuit of definitive answers. “The goal of any business analytics project is not Truth with a capital T, but to convert raw data into insights, inferences or predictive models that can lead to better decisions,” he says. “We must remember what we know about the Law of Large Numbers: You don’t need absolute truth. You just need, ‘true enough to be useful.’”

2. Concerns about data quality. Many organizations think their data needs to be in perfectly clean, consistent shape to begin an analytics program. Consequently, says Lucker, they often defer analytics projects until they’ve constructed an elaborate data warehouse.

“But waiting until those data warehouses have been built can amount to leaving millions of dollars in savings on the table,” he says. “Those savings could have been realized from imperfect and provisional—yet effective—models built with imperfect data. More often than not, something useful can be gleaned even from highly imperfect data.”

3. The pursuit of “perfect” statistical models. While some organizations get hung up on having perfect data, some statisticians lose sight of the practical, business contexts of their modeling projects and get caught up in developing theoretically ideal statistical models, says Guszcza. He believes this tendency stems from some statisticians’ view that mathematical models represent repositories of truth.

“By engaging in a snark hunt for the perfect model and by striving for impractical degrees of accuracy, statisticians sometimes sacrifice the benefits that could result from models that are imperfect but still useful,” says Guszcza.

4. Over-confident analysts. Skilled analysts can be overly confident in their abilities and in the accuracy of their judgments. Though they possess uncommon skills that are sometimes viewed as esoteric, business analytics experts are human, just like the decision makers for whom their models are intended.

For example, Lucker and Guszcza have seen analytics experts opt to build less useful models that confirm to textbook assumptions rather than build more useful models that would require predicting “messier” quantities. Business analytics is a type of science, and therefore textbook knowledge is crucial. But it shouldn’t trump the ultimate business objectives. This often leads to the fifth hurdle organizations face.

5. Lack of communication between data people and decision people. Data modelers and analysts can do more effective work when they maintain an ongoing dialog with the decision makers for whom their work is intended. Frequent, two-way communication helps reduce the risk of unfortunate downstream surprises, expensive implementation snags, and unmet expectations that manifest only at the close of a project, says Lucker.

For example, Lucker and Guszcza say they have been privy to a number of predictive modeling projects that ended badly because the business people “outsourced” required critical thinking entirely to analytics personnel. While skilled, the analytics employees did not have the appropriate perspective to properly design the analysis and interpret the results.

“In more than one case, we witnessed the results of analysts who actually built models to predict the wrong quantity of products—a decision that should have been discussed and signed off on near the beginning of the project,” says Lucker.

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Overcoming these technical, behavioral and organizational barriers can be beneficial in improving the returns your company sees from its analytics initiatives. Given the potential benefits business analytics can offer, doing so is well worth the effort.

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